Optimization of the Hydrological Model Using Multi-objective Particle Swarm Optimization Algorithm

• Published in: 东华大学学报（英文版） Vol. 28; no. 5; pp. 519 - 522
• Main Author: 黄晓敏 雷晓辉 王宇晖 朱连勇
• Format: Journal Article
• Language: English
• Published: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China%State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China%School of Environmental Science and Engineering, Donghua University, Shanghai 201620, China%College of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China 2011; School of Environmental Science and Engineering, Donghua University, Shanghai 201620, China
• Subjects: multi-obiective optimization; hydrological model (HYMOD); multi-objective particle swarm optimization (MOPSO)
• ISSN: 1672-5220

An application of multi-objective particle swarm optimization （MOPSO） algorithm for optimization of the hydrological model （HYMOD） is presented in this paper. MOPSO algorithm is used to find non-dominated solutions with two objectives： high flow Nash-Sutcliffe efficiency and low flow Nash- Sutcliffe efficiency. The two sets＇ coverage rate and Pareto front spacing metric are two criterions to analyze the performance of the algorithms. MOPSO algorithm surpasses multi-objective shuffled complex evolution metcopolis （MOSCEM_UA） algorithr~, in terms of the two sets＇ coverage rate. But when we come to Pareto front spacing rate, the non-dominated solutions of MOSCEM_ UA algorithm are better-distributed than that of MOPSO algorithm when the iteration is set to 40 000. In addition, there are obvious conflicts between the two objectives. But a compromise solution can be acquired by adopting the MOPSO algorithm.